Energy Efficient Service Distribution in Internet of Things

Yosuf, Barzan A.; Musa, Mohamed O. I.; El-Gorashi, Taisir E. H.; Lawey, Ahmed Q.; Elmirghani, Jaafar M. H.

doi:10.1109/icton.2018.8473659

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…e third and final algorithms is designed to solve the problem of optimal placement on the agricultural field and is furnished as shown in Figure 3: e benefit of this proposed algorithm is furnished in the previous section of this work. [21].…”

Section: Return P1[]mentioning

confidence: 99%

Enhanced Secure Technique for Detecting Cyber Attacks Using Artificial Intelligence and Optimal IoT

Kumar¹,

Rahmath

Raju

et al. 2022

Security and Communication Networks

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The Internet of Things (IoT) is a broad term that refers to the collection of information about all of the items that are linked to the Internet. It supervises and controls the functions from a distance, without the need for human interaction. It has the ability to react to the environment either immediately or via its previous experiences. In a similar vein, robots may learn from their experiences in the environment that is relevant to their applications and respond appropriately without the need for human interaction. A greater number of sensors are being distributed across the environment in order to collect and evaluate the essential information. They are gaining ground in a variety of industries, ranging from the industrial environment to the smart home. Sensors are assisting in the monitoring and collection of data from all of the real-time devices that are reliant on all of the different types of fundamental necessities to the most advanced settings available. This research study was primarily concerned with increasing the efficiency of the sensing and network layers of the Internet of Things to increase cyber security. Due to the fact that sensors are resource-constrained devices, it is vital to provide a method for reacting, analysing, and transmitting data collected from the sensors to the base station as efficient as possible. Resource requirements, such as energy, computational power, and storage, vary depending on the kind of sensing devices and communication technologies that are utilised to link real-world objects together. Sensor networks' physical and media access control layers, as well as their applications in diverse geographical and temporal domains, are distinct from one another. Transmission coverage range, energy consumption, and communication technologies differ depending on the application requirements, ranging from low constraints to high resource enrich gadgets. This has a direct impact on the performance of the massive Internet of Things environment, as well as the overall network lifetime of the environment. Identifying and communicating matching items in a massively dispersed Internet of Things environment is critical in terms of spatial identification and communication.

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Section: Return P1[]mentioning

confidence: 99%

Enhanced Secure Technique for Detecting Cyber Attacks Using Artificial Intelligence and Optimal IoT

Kumar¹,

Rahmath

Raju

et al. 2022

Security and Communication Networks

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“…The IoT devices are the sources of the demands and we make the assumption that these demands are flexible in that they can be split among K processing nodes. This assumption is an extension to our previous work in [12]. A service request consists of demands for processing (in MIPS) and bandwidth to transmit data (in Mbps).…”

Section: Performance Evaluation and Resultsmentioning

confidence: 99%

Impact of Distributed Processing on Power Consumption for IoT Based Surveillance Applications

Yosuf

Musa

El-Gorashi

et al. 2019

2019 21st International Conference on Transparent Optical Networks (ICTON)

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With the rapid proliferation of connected devices in the Internet of Things (IoT), the centralized cloud solution faces several challenges, out of which, there is an overwhelming consensus to put energy efficiency at the top of the research agenda. In this paper, we evaluate the impact of demand splitting over heterogeneous processing resources in an IoT platform, supported by Fog and Cloud infrastructure. We develop a Mixed Integer Linear Programming (MILP) model to study the gains of splitting resource intensive demands among IoT nodes, Fog devices and Cloud servers. A surveillance application is considered, which consists of multiple smart cameras capable of capturing and analyzing real-time video streams. The PON access network aggregates IoT layer demands for processing in the Fog, or the Cloud which is accessed through the IP/WDM network. For typical video analysis workloads, the results show that splitting medium demand sizes among IoT and Fog resources yields a total power consumption saving of up to 32%, even if they can host only 10% of the total workload and this can reach 93% for lower number of demands, compared to the centralized cloud solution. However, the gains in power savings from splitting decreases as the number of splits increases.

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“…Next, other proposals related to the scope of our research are described: an approach for power and energy usage for scientific calculation with and without General-Purpose Unit (GPU) acceleration on RPi devices can be found in [39]; energy and execution time of several wearable and mobile devices, including RPi Zero, are compared in [40] with a benchmark to discuss offloading techniques to use for increasing quality of service (QoS) in IoT applications; a preliminary analysis and modeling of energy consumption of evolutionary algorithms in different devices, including RPi, is introduced in [41]; an estimation of energy consumption in transferring data using an IoT protocol over different QoS levels is presented in [42]; a linear IoT model to deploy processes and data to devices and servers in IoT (considering a RPi as a fog node) reducing the total energy consumption of nodes is introduced in [43]; a study about the service distribution in multi-layer IoT architecture to minimize the total energy consumption is presented in [44]; the evolution of the energy consumption of several RPi models is compared to alternative platforms in [45].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Energy Consumption and Optimization Techniques for Writing Energy-Efficient Code

Corral-García¹,

Lemus-Prieto²,

Pérez-Toledano

2019

Electronics

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The unprecedented growth of connected devices, together with the remarkable convergence of a wide variety of technologies, have led to an exponential increase in the services that the internet of things (IoT) can offer, all aimed at improving quality of life. Consequently, in order to meet the numerous challenges this produces, the IoT has become a major subject of research. One of these challenges is the reduction of energy consumption given the significant limitations of some devices. In addition, although the search for energy efficiency was initially focused on hardware, it has become a concern for software developers too. In fact, it has become an intense area of research with the principal objective of analyzing and optimizing the energy consumption of software systems. This research analyzes the energy saving that can be achieved when using a broad set of techniques for writing energy-efficient code for Raspberry Pi devices. It also demonstrates that programmers can save more energy if they apply the proposed techniques manually than when relying on other automatic optimization options offered by the GNU compiler collection (GCC). Thus, it is important that programmers are aware of the significant impact these techniques can have on an application’s energy consumption.

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Energy Efficient Service Distribution in Internet of Things

Cited by 8 publications

References 19 publications

Enhanced Secure Technique for Detecting Cyber Attacks Using Artificial Intelligence and Optimal IoT

Enhanced Secure Technique for Detecting Cyber Attacks Using Artificial Intelligence and Optimal IoT

Impact of Distributed Processing on Power Consumption for IoT Based Surveillance Applications

Analysis of Energy Consumption and Optimization Techniques for Writing Energy-Efficient Code

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